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Designing flexible payment flows with Checkout Session
The post addresses the architectural challenge of taking a digital product business global while supporting localized tax, currencies, one-time purchases, subscriptions, and a branded embedded checkout. It recommends Checkout Sessions as the orchestration layer, with Stripe creating and managing the underlying Payment Intent while declarative parameters handle features such as Stripe Tax, Adaptive Pricing, subscriptions, and promotional codes. The implementation creates a session with ui_mode, mode, automatic_tax, adaptive_pricing, line items, and optional payment_intent_data, then passes the client secret to Stripe Elements for the Payment Element and Currency Selector. This approach supports localized payment methods and currency conversion without a backend rewrite, while direct Payment Intents remain appropriate when an application needs full control over carts, discounts, taxes, or its own checkout abstraction.
Working at the intersection of data and AI with Kenza Boulisfane
Thumbtack software engineer Kenza Boulisfane develops an artificial intelligence tool called the Marketing Analytics Agent to democratize access to complex cross-channel marketing data. Non-technical staff across Data Science, Growth, Brand, and Product can ask natural language performance queries without requiring SQL expertise or manual reporting. A primary technical challenge involves unifying fragmented data across multiple advertising platforms that define metrics differently while maintaining data reliability. The system interprets natural language queries, analyzes underlying performance metrics, and translates results into actionable, data-backed recommendations for next steps. Boulisfane also notes that virtual work environments enhance collaboration by encouraging clearer documentation, intentional communication, and focused individual productivity.
How Chronicle hit #1 on Product Hunt using Notion
Chronicle’s Product Hunt launch aimed for a few hundred upvotes but reached number one and generated a waitlist of more than 100,000 signups. The remote team used Notion as a launch “mission control,” expanding an initial planning page into a database with status and due-date properties, assigned owners, collaborator tags, asset links, social copy, and calendar views. The shared hub coordinated feature timelines, asset publication, emails, LinkedIn posts, announcements, pre-launch instructions, and real-time launch-day updates without losing a single source of truth. Templates turned tasks into reusable pages containing creative files, drafts, and strategy notes, while the broader workspace also stored customer interview transcripts and marketing work. The team attributes the result to early preparation, careful copy and assets, coordinated support from its ecosystem, and treating the launch as a company-wide event; it says the process enabled speed, alignment across time zones, and documented repeatability.
Transformation in action: When support becomes the blueprint for company-wide AI
The post describes how businesses are extending AI beyond customer support across the customer lifecycle, with support teams increasingly positioned to lead that work. Research cited in the 2026 Customer Service Transformation Report says 52% of respondents plan to scale AI to other departments in 2026, while 32% say customer service teams lead their AI transformation strategy. WHOOP deployed Fin on its Join page after prospective buyers faced waits of more than 10 hours; Fin resolved 84% of inbound questions and helped drive a 130% increase in attributable sales. The post warns that isolated departmental agents can fragment experiences, and presents a unified Customer Agent with shared memory and business knowledge as the intended direction. It argues support can provide shared standards, guardrails, operating practices, and implementation expertise as AI expands into sales and other functions.
Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations
Deploying Vision–Language–Action policies on embedded robots is constrained by compute, memory, power, and real-time control requirements; synchronous inference can leave the arm idle and cause oscillatory corrections. NXP presents dataset-recording practices for a tea-bag placement task, including fixed cameras, controlled lighting, a gripper-mounted view, calibration backups, and diverse training and validation splits with recovery episodes. The example uses 120 episodes, three 640×480 cameras at 30 fps, batch size 8, and checkpoint selection based on training and validation success rather than loss alone. For deployment, the VLA is decomposed into vision, LLM backbone, and action-expert stages, with selective quantization and asynchronous scheduling constrained by inference time being shorter than action execution. On the i.MX 95, optimized ACT latency reached 0.32 seconds with 0.89 global accuracy, while ONNX FP32 ACT reached 0.96 global accuracy at 2.86 seconds.
Build agents that run automatically
Cursor Automations introduces always-on agents that run on schedules or react to events from Slack, Linear, GitHub, PagerDuty, or custom webhooks. Each invocation spins up a cloud sandbox, uses configured models and MCPs, verifies its output, and can draw on memory from past runs. The product supports review and monitoring workflows, including security audits on pushes to main, risk-based codeowner assignment, and PagerDuty-driven incident investigation that proposes fixes. Other automations generate weekly repository digests, add tests after merged code, triage Slack bug reports through Linear, and assemble cross-tool dashboards. Examples from Cursor and Rippling report faster incident response and reduced repetitive work, while Runlayer describes a software factory built with Runlayer MCP and plugins.
Log Drains: Now available on Pro
Supabase has launched Log Drains for its Pro tier, allowing users to send Supabase logs to their own logging backend and investigate them alongside the rest of their stack. The feature addresses the separation between Supabase logs and external observability tools by exporting events from Postgres, API Gateway, Auth, Storage, Edge Functions, and Realtime. Logs are sent over HTTP in batches of up to 250 entries or every second, whichever comes first, with Gzip compression when supported by the destination. Supported destinations include Sentry, Grafana Loki, Datadog, AWS S3, Axiom, and generic HTTP endpoints; users configure drains in the dashboard and can create multiple drains, with pricing of $60 per drain per project, $0.20 per million events, and $0.09 per GB of egress.
Supabase Storage: major performance, security, and reliability updates
Supabase Storage has shipped updates live in all regions for projects using Storage, targeting performance at scale, security vulnerabilities, and reliability issues. As projects reached tens of millions of objects, concurrent uploads exposed write bottlenecks, certain SQL deletes could orphan files, and a crafted path could escape the file backend’s storage root. The object-listing rewrite removes the prefixes table, six triggers, and twelve helper functions, derives folder structure from the objects table with a hybrid skip-scan algorithm, and uses cursor-based pagination; deep pagination on 60 million rows is up to 14.8 times faster without a write penalty. Direct SQL deletes are blocked by default through a statement-level trigger, while a configurable 30-second timeout supports cancellation of disconnected clients via native Postgres protocol. Idempotent migrations, TUS lock-race prevention, orphan-scanner fixes, OpenTelemetry metrics, and adapter and API bug fixes are included, with no code changes required.
Introducing Modular Diffusers - Composable Building Blocks for Diffusion Pipelines
Modular Diffusers introduces a composable alternative to Diffusers’ existing DiffusionPipeline for building diffusion workflows from reusable blocks. A ModularPipeline can load a workflow definition, load model components separately with settings such as bfloat16, and preserve the familiar inference API, while blocks for text encoding, image encoding, denoising, and decoding can be inspected, run independently, or rearranged. Custom Python blocks declare components, inputs, outputs, and computation logic; the example adds a Depth Anything V2 depth processor to a Qwen Image ControlNet workflow, with data flowing between compatible blocks. Blocks and complete pipelines can be published to the Hub, and Mellon can generate node interfaces from their consistent definitions. The post presents the system as an early, flexible direction and says Mellon is not ready for production use.
Introducing ClickStack embedded in ClickHouse
ClickHouse 26.2 introduces an embedded distribution of the ClickStack observability UI packaged directly inside the ClickHouse binary. Designed for local exploration and instance inspection, this distribution allows developers to visualize internal query logs, traces, and system metrics without deploying external services. To preserve build integrity and keep the added binary footprint around 4.1 to 4.2 MB, the team avoided a Node.js dependency by embedding a pre-built static Next.js application using CMake-generated C++ structs. Supporting components such as MongoDB, Express, and the Pyodide WebAssembly runtime were omitted, disabling alerting and persistent dashboards in favor of simplicity. Users can access the interface directly over HTTP to monitor local databases and query performance out of the box.
Cursor is now available in JetBrains IDEs
Cursor is now available in IntelliJ IDEA, PyCharm, WebStorm, and other JetBrains IDEs through the Agent Client Protocol (ACP), extending its agent-driven development experience to developers using those environments. The integration lets users choose frontier models from OpenAI, Anthropic, Google, and Cursor, while Cursor provides a custom-built agent harness for each model. Cursor's secure codebase indexing and semantic search are combined with JetBrains IDE code intelligence and tooling to support work on large enterprise codebases. Users install Cursor ACP from the ACP Registry and authenticate with an existing Cursor account; it is free for users on paid plans, and the release is described as a foundation for deeper JetBrains integrations.
PRX Part 3 — Training a Text-to-Image Model in 24h!
PRX Part 3 reports a 24-hour speedrun for training a text-to-image diffusion model, combining techniques that performed well in earlier experiments under a budget of about $1,500 on 32 H200 GPUs. The recipe uses x-prediction in pixel space with 32-pixel patches and a 256-dimensional initial bottleneck, followed by LPIPS and DINOv2 perceptual losses, TREAD token routing, REPA alignment with DINOv3, and Muon for matrix parameters while Adam handles the rest. Training ran for 100,000 steps at 512px with batch size 1,024, then 20,000 steps at 1024px with batch size 512 and no REPA, across three synthetic datasets. The resulting model is described as usable, with strong prompt following and consistent aesthetics, though texture glitches, anatomy issues, and difficulty with hard prompts remain. The post attributes these limitations mainly to undertraining and limited data diversity and presents the recipe as a foundation for further scaling.
How technical support at Cursor uses Cursor
Cursor’s technical support team treats customer investigations as research problems, using a single session to combine code, logs, team knowledge, and past conversations. Engineers typically start in Ask Mode, use semantic search across a locally indexed codebase and documentation, and group related repositories with multi-root workspaces. MCP servers bring customer data, Datadog logs and traces, Slack and support history, runbooks, account details, and ticketing context into those investigations; Linear MCP turns evidence into escalations, while a Slack-mentioned cloud agent can open documentation pull requests. Slash commands, Rules, Skills, and parallel subagents automate repeated work, including failure analysis, known-issue searches, ticket writing, and customer-response drafting. The team reports that more than 75% of support interactions run through Cursor, with stated throughput gains of 5–10x and estimated productivity improvements of up to an order of magnitude.
PlanetScale protects production reliability with Bugbot
PlanetScale describes code review as the bottleneck created by rapidly increasing agent-generated code, while human review capacity remained fixed. To preserve production reliability, the company adopted Bugbot as a dedicated agentic review layer alongside its existing process. Bugbot surfaces semantic and logical issues that static analyzers, linters, and straightforward frontier-model reviews may miss, including premature completion states, non-converging asynchronous controllers, and database restart edge cases. Across more than 2,000 pull requests reviewed monthly, roughly 80% of Bugbot's comments are addressed before merge. PlanetScale says this prevents potential production downtime, saves the equivalent of two full-time engineers of review effort, and lets engineers focus on complex infrastructure work without sacrificing quality.
How we built security into Custom Agents
Custom Agents were designed for collaborative workspaces where useful access must be balanced against dangerous actions. They use a build-from-nothing model with no default read, write, or interaction permissions, then add resource-level controls such as page-level access, Slack “read and reply,” email confirmation, and selected MCP tools. Runtime safeguards combine permission controls with prompt-injection mitigations, monitoring of external data, warnings, owner confirmation for risky actions, and remediation such as deleting an incorrect Slack message. Alpha testing across Notion and customers produced more than 3,000 internal and 25,000 customer agents, and exposed overly broad Slack write access that could reach #general. That finding led to the custom permission and reinforced the case for first-party integrations, while planned work includes admin-approved broader scopes, Workers integrations, and additional permission modes.
Inside MongoDB Dublin: The Heart of Our International Growth
Established in 2013 in Ballsbridge, MongoDB Dublin functions as the company's international headquarters and a primary regional engineering hub. The location employs over 300 individuals spanning more than 40 nationalities across Product and Technology, Sales, and Technical Services teams. Engineers at the hub take direct ownership of core database systems, tackling theoretical computer science problems in areas like query optimization and implementing capabilities like native vector search for embeddings. Benefits provided to employees include 27 days of annual leave, 20 weeks of paid parental leave, and access to internal employee resource groups such as MDBWomen, Queer Collective, and Config. The facility continues to expand its engineering teams to own critical product components rather than peripheral tasks.
Towards Model-based Verification of a Key-Value Storage Engine
MongoDB developers formalized the interface boundary between their distributed transactions protocol and the underlying WiredTiger key-value storage engine using a compositional TLA+ specification. This modular model established an abstract contract to verify that the storage engine's concrete timestamp-based operations matched the semantics expected by the distributed transaction layer. To check conformance, a modified version of the TLC model checker generates a complete graph of reachable states for finite parameters and computes path coverings. Each identified path is translated into an automated test case consisting of a sequence of storage engine API calls. For a finite model with two keys and two transactions, this workflow generated 87,143 test cases that executed against WiredTiger in approximately 40 minutes.
Transformation in action: How AI is evolving support careers
AI is changing customer service operations by shifting human agents away from queue-level work such as triage and FAQs toward training systems, complex escalations, and strategic improvement. In research from The 2026 Customer Service Transformation Report, 45% of teams updated job descriptions with AI-related responsibilities, 40% said agents focus more on training AI systems, and 27% primarily handle complex escalations and edge cases. Interviews with 166 customers found nearly 95% reporting meaningful workflow changes, while 83% saw roles become more strategic and supervisory; mature teams also reported less time handling support volume. New positions including AI knowledge managers and conversation designers are emerging, while existing staff can move into AI-focused work such as optimizing knowledge, monitoring outputs, and improving customer journeys. The source recommends transparent communication, agent participation, dedicated time outside inbox, and metrics such as resolution rate as teams build operating models with AI at their core.
2,000 robots walk into a shop: Simulated A/B testing (2026) - Shopify
Shopify’s SimGym addresses the difficulty of evaluating storefront changes when merchants lack enough traffic for conventional A/B tests, by simulating shopping sessions with LLM-guided robots. Each robot runs in a cloud Chromium browser with a persona, budget, and intent, while a counterpart tests the alternate theme; the system uses real page representations rather than a mocked DOM. To support bursty, sequential workloads with 89K–127K accumulated tokens per session, Shopify self-hosted gpt-oss-120b on 48 NVIDIA B200 GPUs, combining MXFP4 expert quantization, FP8 KV-cache, FlashInfer kernels, async scheduling, and guided decoding. Production measurements report 80K to 88K tokens/second per B200 from async scheduling, a 57% reduction in HTTP/gRPC overhead, and MIG experiments yielding about 20% lower latency and 12% higher daily throughput without quality regression; speculative decoding is validated but queued.
The third era of AI software development
The post describes a third era of AI-assisted software development, in which autonomous agents handle larger tasks over longer timescales while developers define problems and review results. It traces a progression from Tab autocomplete, to synchronous prompt-and-response agents, and then cloud-based agents running on separate virtual machines. These agents can iterate and test for hours, returning logs, video recordings, and live previews that make parallel work and artifact-based review practical. Cursor reports that 35% of its internally merged pull requests are created by agents operating autonomously in cloud VMs, and that agent users now outnumber Tab users two-to-one after usage grew more than 15x in a year. The post says broader adoption still depends on reliable tests, environments, tools, and context, while developers shift toward decomposition, review, and feedback rather than line-by-line guidance.
Top topics
- Architecture834
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- Open Source563
- AI Agents553
- AI469
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